eli_data <- clean_data_eli %>%
select(sub_id, eli_number, eli_targ_pmc, eli_self_pmc, itt_comp_gmc,
target_condition, eli_targ, eli_self, eli_stereo, eli_stereo_pmc,
analog_condition, itt_comp) %>%
unique() %>%
na.omit()
eli5_data <- clean_data_eli %>%
filter(eli_number %in% c("1", "2", "3", "4", "5")) %>%
select(sub_id, eli_number, eli_targ_pmc, eli_self_pmc, itt_comp_gmc,
target_condition, eli_targ, eli_self, eli_stereo, eli_stereo_pmc,
analog_condition, itt_comp) %>%
unique() %>%
na.omit()
eli_wide_self <- clean_data_eli %>%
select(sub_id, eli_number, eli_self, pol_orient_1, pol_orient_2, pol_orient_3) %>%
unique() %>%
pivot_wider(names_from = eli_number, values_from = eli_self) %>%
rename("Politics: Overall" = pol_orient_1,
"Politics: Social" = pol_orient_2,
"Politics: Economic" = pol_orient_3,
"ElI 1" = `1`,
"ElI 2" = `2`,
"ElI 3" = `3`,
"ElI 4" = `4`,
"ElI 5" = `5`,
"ElI 6" = `6`,
"ElI 7" = `7`,
"ElI 8" = `8`,
"ElI 9" = `9`,
"ElI 10" = `10`)
# These look the same as in the PSPB paper
hist(eli_wide_self$`ElI 1`)
hist(eli_wide_self$`ElI 2`)
hist(eli_wide_self$`ElI 3`)
hist(eli_wide_self$`ElI 4`)
hist(eli_wide_self$`ElI 5`)
eli_self_cor <- eli_wide_self %>%
select(-sub_id)
eli_pol_cor <- cor(eli_self_cor)
corrplot(eli_pol_cor,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
Responses on the ELI are not related to responses on the political orientation questions nor each other (aka orthogonal)
eli_wide_targ <- clean_data_eli %>%
select(sub_id, eli_number, eli_targ, pol_orient_1, pol_orient_2, pol_orient_3) %>%
unique() %>%
pivot_wider(names_from = eli_number, values_from = eli_targ) %>%
select(-sub_id) %>%
rename("Politics: Overall" = pol_orient_1,
"Politics: Social" = pol_orient_2,
"Politics: Economic" = pol_orient_3,
"ElI 1" = `1`,
"ElI 2" = `2`,
"ElI 3" = `3`,
"ElI 4" = `4`,
"ElI 5" = `5`,
"ElI 6" = `6`,
"ElI 7" = `7`,
"ElI 8" = `8`,
"ElI 9" = `9`,
"ElI 10" = `10`)
eli_targ_matrix_targ <- cor(eli_wide_targ)
corrplot(eli_targ_matrix_targ,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
The target has slightly higher correlations overall for the ELI, but lower ones in relation to political orientation
eli_wide_stereo <- clean_data_eli %>%
select(sub_id, eli_number, eli_stereo, pol_orient_1, pol_orient_2, pol_orient_3) %>%
unique() %>%
pivot_wider(names_from = eli_number, values_from = eli_stereo) %>%
rename("Politics: Overall" = pol_orient_1,
"Politics: Social" = pol_orient_2,
"Politics: Economic" = pol_orient_3,
"ElI 1" = `1`,
"ElI 2" = `2`,
"ElI 3" = `3`,
"ElI 4" = `4`,
"ElI 5" = `5`,
"ElI 6" = `6`,
"ElI 7" = `7`,
"ElI 8" = `8`,
"ElI 9" = `9`,
"ElI 10" = `10`) %>%
select(-sub_id)
eli_stereo_matrix_stereo <- cor(eli_wide_stereo)
corrplot(eli_stereo_matrix_stereo,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
Again, higher correlations than for the self, but still only .35 as the highest. Low correlations with politics, but higher than with the target.
https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html
eli_randint_test <- lmer(eli_targ_pmc ~ eli_self_pmc + # itt does not work as a RE; model does not converge
(1 | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(eli_randint_test)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc + (1 | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12331.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0103 -0.5900 0.0054 0.6712 3.4945
##
## Random effects:
## Groups Name Variance
## sub_id (Intercept) 0.00000000000000000000000000000001469
## Residual 1.07012291677120230382058707618853077
## Std.Dev.
## 0.0000000000000001212
## 1.0344674556365716089
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.000000000000000006571 0.015886707663029700499 0.000
## eli_self_pmc 0.009365790070278842347 0.012313692726427382870 0.761
##
## Correlation of Fixed Effects:
## (Intr)
## eli_slf_pmc 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
The random variance for the intercept is 0, which is causing the singularity. This does not occur with the BFI. The data looks normal in the descriptives document. Checking some more stuff below.
Person mean centered variables
eli_coeffs_per_sub_c <- lmList(eli_targ_pmc ~ 1 + eli_self_pmc | sub_id, eli_data)
eli_coeffs_per_sub_c
## Call:
## Model: eli_targ_pmc ~ 1 + eli_self_pmc | sub_id
## Data: eli_data
##
## Coefficients:
## (Intercept) eli_self_pmc
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##
## Degrees of freedom: 4240 total; 3392 residual
## Residual standard error: 1.05803
Uncentered variables
eli_coeffs_per_sub <- lmList(eli_targ ~ 1 + eli_self | sub_id, eli_data)
eli_coeffs_per_sub
## Call:
## Model: eli_targ ~ 1 + eli_self | sub_id
## Data: eli_data
##
## Coefficients:
## (Intercept) eli_self
## 1 4.0677966101694913447773 -0.271186440677966267287502
## 2 2.3281250000000000000000 0.132812499999999916733273
## 3 3.3214285714285707307170 -0.035714285714285698425385
## 4 2.7434944237918204024140 0.308550185873606108710732
## 5 2.7272727272727270708685 0.204545454545454530315141
## 6 2.2096069868995620311125 0.222707423580785879302368
## 8 4.8196721311475414495362 -0.491803278688524636574897
## 10 3.5692307692307680966337 -0.076923076923076788569134
## 11 1.0000000000000013322676 0.749999999999999777955395
## 12 2.7999999999999984900967 0.000000000000000219427092
## 13 3.0000000000000004440892 0.125000000000000083266727
## 14 3.5100671140939598835473 -0.026845637583892714012057
## 15 4.2483221476510060199416 -0.328859060402684477697477
## 16 2.0567375886524805750355 0.390070921985815610710802
## 17 2.9999999999999991118216 0.000000000000000052012348
## 19 4.1874999999999991118216 -0.562500000000000222044605
## 20 2.6714285714285712636240 0.142857142857142876968268
## 21 2.9999999999999960031971 0.000000000000000672553716
## 22 2.9999999999999991118216 -0.000000000000000024212645
## 23 2.9999999999999991118216 0.000000000000000000000000
## 25 4.1923076923076916244781 -0.192307692307692262856378
## 26 3.5140845070422530582732 -0.239436619718309706694725
## 27 3.6763485477178421412248 -0.265560165975103679159020
## 29 3.2173913043478266082786 0.072463768115941920577860
## 30 0.6938775510204090446464 0.693877551020408045445720
## 32 2.9999999999999991118216 -0.000000000000000084825507
## 33 3.8837209302325574888926 -0.124031007751938024408211
## 34 3.8993288590604020527053 -0.281879194630872520477283
## 35 3.4617940199335550666149 -0.109634551495016580036079
## 36 3.6666666666666674068153 -0.190476190476190937461709
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## 38 2.8859060402684550972197 0.073825503355704688579486
## 39 2.9285714285714274929262 0.132653061224489832170548
## 40 2.2758620689655177926625 0.206896551724137872652065
## 41 2.1562500000000000000000 0.218749999999999861222122
## 42 2.7377049180327852617722 0.049180327868852811989964
## 43 2.9090909090909091716526 0.053030303030303080347174
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## 59 2.9153439153439153486147 -0.005291005291005274231708
## 60 1.8529411764705878695736 0.401960784313725505434434
## 61 3.7209302325581399273347 -0.279069767441860572265711
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## 66 4.4899598393574295940311 -0.341365461847389528582397
## 67 3.2281879194630858087578 -0.161073825503355472221756
## 68 2.1311475409836058148016 0.352459016393442792214330
## 69 3.0000000000000008881784 -0.000000000000000208766557
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## 72 3.5961538461538458122391 -0.057692307692307667754683
## 73 2.7846889952153097702592 0.133971291866028574535363
## 74 6.0434782608695645222951 -0.956521739130434700548733
## 75 3.7796610169491526853847 -0.118644067796610297449433
## 76 5.0317460317460307450688 -0.349206349206349242475511
## 78 0.2690763052208833272516 0.714859437751004023198220
## 80 3.6000000000000005329071 -0.333333333333333481363070
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## 84 2.9999999999999986677324 0.000000000000000110143795
## 85 4.9830508474576271638057 -0.432203389830508433178125
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## 88 2.9999999999999991118216 -0.000000000000000020352658
## 89 2.9897959183673465943798 -0.153061224489795977277140
## 90 3.2837837837837828836030 -0.101351351351351537388723
## 91 3.0000000000000000000000 -0.000000000000000143663804
## 92 2.5000000000000000000000 0.272727272727272873620308
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## 447 4.7846153846153844924061 -0.538461538461538435917930
## 448 3.1162790697674416229290 0.124031007751937982774848
## 449 3.0698689956331874917339 -0.196506550218340486635427
## 450 3.6937799043062198300902 -0.320574162679425900268626
## 451 0.9405405405405404817287 0.702702702702702963755144
## 452 4.0185185185185181566681 -0.435185185185185119394191
## 453 2.4999999999999995559108 0.235294117647058736997323
## 454 1.6285714285714276705619 0.591836734693877764001968
## 455 4.4658385093167689561255 -0.322981366459627328158177
## 456 2.9999999999999991118216 -0.000000000000000060473687
## 457 2.0737704918032782153148 0.163934426229508156680481
## 459 4.4999999999999991118216 -0.599999999999999977795540
## 460 0.8225806451612905911830 0.758064516129032139879484
## 461 1.9999999999999991118216 0.294117647058823594719001
## 462 4.6842105263157893801917 -0.526315789473684625399130
## 463 2.9545454545454550299155 -0.189393939393939392257238
## 464 4.4673913043478261641894 -0.402173913043478492568283
## 474 3.0571428571428564957557 0.214285714285714412596917
## 476 2.6538461538461537436717 0.096153846153846117550401
## 481 2.1000000000000000888178 0.249999999999999916733273
## 482 3.3057851239669422405143 -0.082644628099173611524009
## 483 4.6243093922651929972290 -0.303867403314917128298589
## 489 2.2318840579710137461689 0.202898550724637277697937
## 498 3.4054054054054048172873 0.027027027027027018118988
## 501 3.3521594684385385143344 -0.076411960132890408003981
## 508 3.7195121951219514144782 -0.060975609756097469416058
## 510 3.0745341614906820382203 0.068322981366459645258260
## 512 3.1063829787234040757937 0.028368794326241151410040
## 515 2.7999999999999993782751 0.142857142857142627168088
## 517 2.7173913043478261641894 0.108695652173912846172321
## 520 2.3119266055045870622564 0.330275229357798238982724
## 523 2.6410256410256409687065 0.128205128205128249252454
## 527 4.2375690607734801673701 -0.132596685082872922656350
## 530 2.6363636363636366866103 0.140495867768595072977433
## 536 5.3472222222222214327303 -0.513888888888888950567946
## 540 4.1666666666666660745477 -0.254901960784313763586084
##
## Degrees of freedom: 4240 total; 3392 residual
## Residual standard error: 1.05803
There seems to be variability in the intercepts, even though lmer is not finding it.
ggplot(clean_data_eli, aes(eli_self_pmc, eli_targ_pmc)) +
geom_point()
eli_randint_only <- lmer(eli_targ_pmc ~ 1 + # itt does not work as a RE; model does not converge
(1 | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(eli_randint_only)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ 1 + (1 | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12325
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9969 -0.5800 0.0000 0.6767 3.4802
##
## Random effects:
## Groups Name Variance
## sub_id (Intercept) 0.00000000000000000000000000000001469
## Residual 1.07001651332861880128177745064022020
## Std.Dev.
## 0.0000000000000001212
## 1.0344160252667293776
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.000000000000000006703 0.015885917827403422953 0
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
These were the ones used in the PSPB paper
eli5_randint_test <- lmer(eli_targ_pmc ~ eli_self_pmc + # itt does not work as a RE; model does not converge
(1 | sub_id),
data = eli5_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(eli5_randint_test)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc + (1 | sub_id)
## Data: eli5_data
##
## REML criterion at convergence: 5708.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2955 -0.4688 -0.0294 0.6000 3.5763
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id (Intercept) 0.0000 0.0000
## Residual 0.8605 0.9276
## Number of obs: 2120, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.25528 0.02017 12.65
## eli_self_pmc -0.01700 0.01650 -1.03
##
## Correlation of Fixed Effects:
## (Intr)
## eli_slf_pmc -0.050
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
eli_randslopes_test <- lmer(eli_targ_pmc ~ eli_self_pmc + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc | sub_id),
data = eli_data)
summary(eli_randslopes_test)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc + (0 + eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12253.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0943 -0.5982 0.0068 0.6452 3.2360
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.04537 0.2130
## Residual 0.99461 0.9973
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.000000000000000005827 0.015315897842633369522 0.000
## eli_self_pmc 0.012234199608380517260 0.016068790172722027115 0.761
##
## Correlation of Fixed Effects:
## (Intr)
## eli_slf_pmc 0.000
Running without the random intercept fixes the issue
comp_eli_randslopes <- lmer(eli_targ_pmc ~ eli_self_pmc*itt_comp_gmc + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(comp_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc * itt_comp_gmc + (0 + eli_self_pmc |
## sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12233
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2229 -0.6049 0.0141 0.6519 3.1456
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.03695 0.1922
## Residual 0.99503 0.9975
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 0.00000000000000000569 0.01531913279267939354
## eli_self_pmc 0.01357934379323691258 0.01540005841078596720
## itt_comp_gmc 0.00000000000000001164 0.01430741929851506340
## eli_self_pmc:itt_comp_gmc -0.08568263438990457448 0.01433422509918278950
## t value
## (Intercept) 0.000
## eli_self_pmc 0.882
## itt_comp_gmc 0.000
## eli_self_pmc:itt_comp_gmc -5.977
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ itt_c_
## eli_slf_pmc 0.000
## itt_cmp_gmc 0.000 0.000
## el_slf_p:__ 0.000 -0.017 0.000
tab_model(comp_eli_randslopes,
digits = 3)
| eli_targ_pmc | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.000 | -0.030 – 0.030 | 1.000 |
| eli self pmc | 0.014 | -0.017 – 0.044 | 0.378 |
| itt comp gmc | 0.000 | -0.028 – 0.028 | 1.000 |
|
eli self pmc * itt comp gmc |
-0.086 | -0.114 – -0.058 | <0.001 |
| Random Effects | |||
| σ2 | 1.00 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.04 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.06 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.013 / 0.071 | ||
threat_levels = list(itt_comp_gmc = c(-1.07, 0.0, 1.07))
comp_simpslopes_eli <- emtrends(comp_eli_randslopes, ~ itt_comp_gmc,
var ="eli_self_pmc",
at = threat_levels)
comp_simpslopes_eli
## itt_comp_gmc eli_self_pmc.trend SE df asymp.LCL asymp.UCL
## -1.07 0.1053 0.0219 Inf 0.0623 0.1482
## 0.00 0.0136 0.0154 Inf -0.0166 0.0438
## 1.07 -0.0781 0.0215 Inf -0.1203 -0.0359
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
test(comp_simpslopes_eli)
## itt_comp_gmc eli_self_pmc.trend SE df z.ratio p.value
## -1.07 0.1053 0.0219 Inf 4.802 <.0001
## 0.00 0.0136 0.0154 Inf 0.882 0.3779
## 1.07 -0.0781 0.0215 Inf -3.624 0.0003
##
## Degrees-of-freedom method: asymptotic
pairs(comp_simpslopes_eli)
## contrast estimate SE df z.ratio p.value
## (-1.07) - 0 0.0917 0.0153 Inf 5.977 <.0001
## (-1.07) - 1.07 0.1834 0.0307 Inf 5.977 <.0001
## 0 - 1.07 0.0917 0.0153 Inf 5.977 <.0001
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimates
comp_eli_maineffect <- effect("eli_self_pmc:itt_comp_gmc",
xlevels = list(itt_comp_gmc = c(-1.07, 0, 1.07)),
mod = comp_eli_randslopes)
comp_eli_maineffect <- as.data.frame(comp_eli_maineffect)
comp_eli_maineffect$itt_comp_gmc <- as.factor(comp_eli_maineffect$itt_comp_gmc)
ggplot(comp_eli_maineffect, aes(eli_self_pmc, fit, group = itt_comp_gmc)) +
geom_smooth(method = "lm",
size = .7,
se = FALSE,
colour = "black",
aes(linetype = itt_comp_gmc)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
scale_linetype_manual("Target-level threat",
breaks = c(-1.07, 0, 1.07),
labels = c("Low",
"Average",
"High"),
values = c("solid",
"dashed",
"dotted")) +
labs(x = "ELI responses for self",
y = "ELI responses for target")
# checking normality of conditional residuals
qqnorm(residuals(comp_eli_randslopes), main="Q-Q plot for conditional residuals")
# checking the normality of the random effects (here random intercept):
qqnorm(ranef(comp_eli_randslopes)$sub_id$eli_self_pmc,
main="Q-Q plot for the self random effect")
plot_model(comp_eli_randslopes, type='diag')
## [[1]]
##
## [[2]]
## [[2]]$sub_id
##
##
## [[3]]
##
## [[4]]
Also seems evenly spread but diagonal
cond_eli_randslopes <- lmer(eli_targ_pmc ~ eli_self_pmc*target_condition + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(cond_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc * target_condition + (0 + eli_self_pmc |
## sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12232.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2042 -0.6027 0.0235 0.6548 3.1413
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.03621 0.1903
## Residual 0.99469 0.9973
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate
## (Intercept) -0.000000000000000003719
## eli_self_pmc 0.137220932631810288438
## target_conditionLOSS 0.000000000000000008909
## target_conditionWARM 0.000000000000000019245
## eli_self_pmc:target_conditionLOSS -0.242898226881491041684
## eli_self_pmc:target_conditionWARM -0.150822064992466559064
## Std. Error t value
## (Intercept) 0.025665826389154600129 0.000
## eli_self_pmc 0.025726227692130149149 5.334
## target_conditionLOSS 0.037892422327729401166 0.000
## target_conditionWARM 0.036670523227181700543 0.000
## eli_self_pmc:target_conditionLOSS 0.037882423473722962037 -6.412
## eli_self_pmc:target_conditionWARM 0.036775376150086649951 -4.101
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ t_LOSS t_WARM e__:_L
## eli_slf_pmc 0.000
## trgt_cnLOSS -0.677 0.000
## trgt_cnWARM -0.700 0.000 0.474
## el_s_:_LOSS 0.000 -0.679 0.000 0.000
## el_s_:_WARM 0.000 -0.700 0.000 0.000 0.475
tab_model(cond_eli_randslopes,
digits = 3)
| eli_targ_pmc | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.000 | -0.050 – 0.050 | 1.000 |
| eli self pmc | 0.137 | 0.087 – 0.188 | <0.001 |
| target condition [LOSS] | 0.000 | -0.074 – 0.074 | 1.000 |
| target condition [WARM] | 0.000 | -0.072 – 0.072 | 1.000 |
|
eli self pmc * target condition [LOSS] |
-0.243 | -0.317 – -0.169 | <0.001 |
|
eli self pmc * target condition [WARM] |
-0.151 | -0.223 – -0.079 | <0.001 |
| Random Effects | |||
| σ2 | 0.99 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.04 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.06 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.016 / 0.072 | ||
targ_levels <-list(target_condition = c("CONTROL", "LOSS", "WARM"))
simpslopes_eli_nostereo_cond <- emtrends(cond_eli_randslopes, ~ target_condition,
var ="eli_self_pmc",
at = targ_levels)
simpslopes_eli_nostereo_cond
## target_condition eli_self_pmc.trend SE df asymp.LCL asymp.UCL
## CONTROL 0.1372 0.0257 Inf 0.0868 0.1876
## LOSS -0.1057 0.0278 Inf -0.1602 -0.0512
## WARM -0.0136 0.0263 Inf -0.0651 0.0379
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
pairs(simpslopes_eli_nostereo_cond)
## contrast estimate SE df z.ratio p.value
## CONTROL - LOSS 0.2429 0.0379 Inf 6.412 <.0001
## CONTROL - WARM 0.1508 0.0368 Inf 4.101 0.0001
## LOSS - WARM -0.0921 0.0383 Inf -2.407 0.0426
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimates
test(simpslopes_eli_nostereo_cond)
## target_condition eli_self_pmc.trend SE df z.ratio p.value
## CONTROL 0.1372 0.0257 Inf 5.334 <.0001
## LOSS -0.1057 0.0278 Inf -3.800 0.0001
## WARM -0.0136 0.0263 Inf -0.518 0.6048
##
## Degrees-of-freedom method: asymptotic
# checking normality of conditional residuals
qqnorm(residuals(cond_eli_randslopes), main="Q-Q plot for conditional residuals")
# checking the normality of the random effects
qqnorm(ranef(cond_eli_randslopes)$sub_id$eli_self_pmc,
main="Q-Q plot for the self random effect")
plot_model(cond_eli_randslopes, type='diag')
## [[1]]
##
## [[2]]
## [[2]]$sub_id
##
##
## [[3]]
##
## [[4]]
Heavy tail?
Also seems evenly spread but diagonal
eli_data %<>%
mutate(target_condition = forcats::fct_relevel(target_condition, c("CONTROL", "WARM", "LOSS")))
target_labels <- c("CONTROL" = "Control target",
"WARM" = "Warm target",
"LOSS" = "Loss target")
eli_cond_df <- effect("eli_self_pmc:target_condition",
xlevels = list(target_condition = c("CONTROL",
"WARM",
"LOSS")),
mod = cond_eli_randslopes)
eli_cond_df <- as.data.frame(eli_cond_df)
eli_cond_df$target_condition <- as.factor(eli_cond_df$target_condition)
ggplot(eli_cond_df, aes(eli_self_pmc, fit, group = target_condition)) +
geom_smooth(method = "lm",
size = .7,
se = FALSE,
colour = "black",
aes(linetype = target_condition)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
scale_linetype_manual("Target Variable",
breaks = c("CONTROL", "WARM", "LOSS"),
labels = c("Least threatening",
"Medium threatening",
"High threatening"),
values = c("solid",
"dashed",
"dotted"))
labs(x = "ELI responses for self",
y = "ELI responses for target")
## $x
## [1] "ELI responses for self"
##
## $y
## [1] "ELI responses for target"
##
## attr(,"class")
## [1] "labels"
analogcomp_eli_randslopes <- lmer(eli_targ ~ eli_self_pmc*analog_condition*itt_comp_gmc +
(0 + eli_self_pmc | sub_id), data = eli_data)
summary(analogcomp_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ ~ eli_self_pmc * analog_condition * itt_comp_gmc + (0 +
## eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12722.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.51750 -0.68352 -0.07376 0.76725 2.57997
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.0289 0.170
## Residual 1.1260 1.061
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.127608 0.023557 132.767
## eli_self_pmc 0.004498 0.022279 0.202
## analog_conditioncontrol 0.035595 0.032696 1.089
## itt_comp_gmc -0.118886 0.022344 -5.321
## eli_self_pmc:analog_conditioncontrol 0.020146 0.030791 0.654
## eli_self_pmc:itt_comp_gmc -0.059057 0.020877 -2.829
## analog_conditioncontrol:itt_comp_gmc 0.042211 0.030584 1.380
## eli_self_pmc:analog_conditioncontrol:itt_comp_gmc -0.051639 0.028680 -1.801
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ anlg_c itt_c_ el__:_ e__:__ an_:__
## eli_slf_pmc 0.000
## anlg_cndtnc -0.721 0.000
## itt_cmp_gmc 0.073 0.000 -0.053
## el_slf_pm:_ 0.000 -0.724 0.000 0.000
## el_slf_p:__ 0.000 0.047 0.000 0.000 -0.034
## anlg_cnd:__ -0.053 0.000 0.007 -0.731 0.000 0.000
## el_sl_:_:__ 0.000 -0.034 0.000 0.000 -0.012 -0.728 0.000
tab_model(analogcomp_eli_randslopes)
| eli_targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.13 | 3.08 – 3.17 | <0.001 |
| eli self pmc | 0.00 | -0.04 – 0.05 | 0.840 |
|
analog condition [control] |
0.04 | -0.03 – 0.10 | 0.276 |
| itt comp gmc | -0.12 | -0.16 – -0.08 | <0.001 |
|
eli self pmc * analog condition [control] |
0.02 | -0.04 – 0.08 | 0.513 |
|
eli self pmc * itt comp gmc |
-0.06 | -0.10 – -0.02 | 0.005 |
|
analog condition [control] * itt comp gmc |
0.04 | -0.02 – 0.10 | 0.168 |
|
(eli self pmc * analog condition [control]) * itt comp gmc |
-0.05 | -0.11 – 0.00 | 0.072 |
| Random Effects | |||
| σ2 | 1.13 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.04 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.022 / 0.063 | ||
analogcond_eli_randslopes <- lmer(eli_targ ~ eli_self_pmc*analog_condition*target_condition +
(0 + eli_self_pmc | sub_id), data = eli_data)
summary(analogcond_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ ~ eli_self_pmc * analog_condition * target_condition +
## (0 + eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12695.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.46976 -0.62515 -0.04895 0.75378 2.64903
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.03014 0.1736
## Residual 1.11583 1.0563
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 3.3126761 0.0396434
## eli_self_pmc 0.1419875 0.0380866
## analog_conditioncontrol -0.0226761 0.0544646
## target_conditionWARM -0.2010819 0.0564690
## target_conditionLOSS -0.3439261 0.0575768
## eli_self_pmc:analog_conditioncontrol -0.0092576 0.0518635
## eli_self_pmc:target_conditionWARM -0.1654422 0.0538018
## eli_self_pmc:target_conditionLOSS -0.2487041 0.0552807
## analog_conditioncontrol:target_conditionWARM 0.1084503 0.0777930
## analog_conditioncontrol:target_conditionLOSS -0.0007614 0.0803329
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 0.0266920 0.0740320
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 0.0120479 0.0762011
## t value
## (Intercept) 83.562
## eli_self_pmc 3.728
## analog_conditioncontrol -0.416
## target_conditionWARM -3.561
## target_conditionLOSS -5.973
## eli_self_pmc:analog_conditioncontrol -0.178
## eli_self_pmc:target_conditionWARM -3.075
## eli_self_pmc:target_conditionLOSS -4.499
## analog_conditioncontrol:target_conditionWARM 1.394
## analog_conditioncontrol:target_conditionLOSS -0.009
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 0.361
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 0.158
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ anlg_c t_WARM t_LOSS el__:_ e__:_W e__:_L a_:_WA
## eli_slf_pmc 0.000
## anlg_cndtnc -0.728 0.000
## trgt_cnWARM -0.702 0.000 0.511
## trgt_cnLOSS -0.689 0.000 0.501 0.483
## el_slf_pm:_ 0.000 -0.734 0.000 0.000 0.000
## el_s_:_WARM 0.000 -0.708 0.000 0.000 0.000 0.520
## el_s_:_LOSS 0.000 -0.689 0.000 0.000 0.000 0.506 0.488
## anlg_:_WARM 0.510 0.000 -0.700 -0.726 -0.351 0.000 0.000 0.000
## anlg_:_LOSS 0.493 0.000 -0.678 -0.346 -0.717 0.000 0.000 0.000 0.475
## e__:_:_WARM 0.000 0.514 0.000 0.000 0.000 -0.701 -0.727 -0.354 0.000
## e__:_:_LOSS 0.000 0.500 0.000 0.000 0.000 -0.681 -0.354 -0.725 0.000
## a_:_LO e__:_:_W
## eli_slf_pmc
## anlg_cndtnc
## trgt_cnWARM
## trgt_cnLOSS
## el_slf_pm:_
## el_s_:_WARM
## el_s_:_LOSS
## anlg_:_WARM
## anlg_:_LOSS
## e__:_:_WARM 0.000
## e__:_:_LOSS 0.000 0.477
tab_model(analogcond_eli_randslopes)
| eli_targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.31 | 3.23 – 3.39 | <0.001 |
| eli self pmc | 0.14 | 0.07 – 0.22 | <0.001 |
|
analog condition [control] |
-0.02 | -0.13 – 0.08 | 0.677 |
| target condition [WARM] | -0.20 | -0.31 – -0.09 | <0.001 |
| target condition [LOSS] | -0.34 | -0.46 – -0.23 | <0.001 |
|
eli self pmc * analog condition [control] |
-0.01 | -0.11 – 0.09 | 0.858 |
|
eli self pmc * target condition [WARM] |
-0.17 | -0.27 – -0.06 | 0.002 |
|
eli self pmc * target condition [LOSS] |
-0.25 | -0.36 – -0.14 | <0.001 |
|
analog condition [control] * target condition [WARM] |
0.11 | -0.04 – 0.26 | 0.163 |
|
analog condition [control] * target condition [LOSS] |
-0.00 | -0.16 – 0.16 | 0.992 |
|
(eli self pmc * analog condition [control]) * target condition [WARM] |
0.03 | -0.12 – 0.17 | 0.718 |
|
(eli self pmc * analog condition [control]) * target condition [LOSS] |
0.01 | -0.14 – 0.16 | 0.874 |
| Random Effects | |||
| σ2 | 1.12 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.04 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.031 / 0.072 | ||
cor_predictors_eli <- clean_data_eli %>%
select(sub_id, eli_number, eli_self, eli_targ, itt_comp) %>%
unique() %>%
na.omit() %>%
select(eli_self, eli_targ, itt_comp) %>%
rename("ELI: Self" = eli_self,
"ELI: Target" = eli_targ,
"Threat Composite" = itt_comp)
cor_matrix_predictors_eli <- cor(cor_predictors_eli)
corrplot(cor_matrix_predictors_eli,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50',
type = 'lower')